[lme4 package] Does the order of rows(trials) in a data frame (long format) affect the results of the lmer model (maybe somehow)?
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
On 15-01-07 07:13 PM, Zhaohong wrote:
*I believe that the order shouldn't matter, but I don't know why I got different results after fitting the same model in a data frame that is ordered differently. *
We have seen one case where the order does change the results slightly (https://github.com/lme4/lme4/issues/262) , and have a not-yet-reproducible report (see prev link) of a case where the order changes the standard error estimates considerably more. That the order affects the results is surprising, but believable; I/we haven't had a chance yet to dig through and figure out how the ordering could change the linear algebra, but clearly it does. A couple of comments: * the factor names are different in your two examples (A/B/C vs RanOrCt/Direction/Running etc.) -- that makes me mildly suspicious that there might be some other difference in the input data, but maybe you just forgot to rewrite something. Probably the simplest way to double check these results is to do an experiment where you input the results from each case as starting values (e.g. update(model1,start=getME(model2,"theta"))) and see what happens. Hopefully starting from each of those (very similar) starting points will get you to the same starting point. It would also be worth trying a different optimizer (see https://rpubs.com/bbolker/lme4trouble1 for examples). Having a random effect with only 6 levels (Category) is pushing the envelope a bit, especially as you're trying to fit an 8x8 variance-covariance matrix ((A*B*C|Category)); you might try recasting that as a fixed effect.
*Here are my R codes. Did I use the order() function wrongly such that I got different results?* *The lmer model fitted with the data frame before it is ordered (There were no warning messages, and the model was able to converge.):* *Model1<-lmer(Stimulus.RT~ 1+A*B*C+(1+A|Subject)+(1+A*B*C|Item)+(1+A*B*C|Category), data=edata,verbose=2,control=lmerControl(optCtrl=list(maxfun=500000)))* *The verbose results are as follows:* npt = 77 , n = 75 rhobeg = 0.2 , rhoend = 2e-07 0.020: 86: -783.903;0.492578 -0.0462110 -0.0363706 -0.0207690 -0.00431843 0.00131644 -0.00481740 -0.00308283 0.502089 -0.0211198 -0.0336018 -0.0237567 -0.00155808 -0.0307396 -0.000390386 0.354830 -0.0308058 -0.0351383 -0.0137964 -0.0238208 -0.0133714 0.305507 -0.0174871 -0.0328382 -0.00155512 0.00996183 0.646703 -0.0258383 -0.00140789 -0.00146090 0.593574 -0.0176632 -0.0351208 0.611448 0.000434827 0.819591 0.735976 -0.105154 0.553306 0.948775 -0.00950248 -0.00976528 -0.0113362 -0.00949456 -0.00980388 -0.0100146 -0.00779947 0.946928 -0.00984788 -0.0101504 -0.00957164 -0.00986887 -0.00987130 -0.00960832 0.946926 -0.0101303 -0.0103410 -0.00976277 -0.00978197 -0.00937871 0.947412 -0.00988796 -0.0100200 -0.0103097 -0.0103002 0.949607 -0.0106541 -0.00943222 -0.0111189 0.947582 -0.0104333 -0.00883065 0.949409 -0.0119380 0.954371
From comparing with the results below, it looks like the first report
(at step "0.020: 86") is identical between versions, but that the two
versions have diverged slightly by the second report ("0.0020: 1139" in
one case, "0.0020: 1266" in the other)
0.0020: 1266: -1011.94;0.450579 -0.0561922 -0.0390931 0.00888335 0.0594143 0.0967819 0.0600815 0.0674752 0.220653 0.00904686 -0.0695420 -0.0628733 0.117864 -0.0596009 0.0799141 0.229251 -0.0664593 -0.123684 0.0490019 -0.0235120 -0.0541726 0.118324 -0.0741990 -0.0809683 0.0958857 0.268214 0.249829 0.0324988 0.114487 0.128747 0.0173838 0.0167991 -0.00732449 0.0501891 0.0850320 0.157297 0.746053 -0.130440 0.331480 0.293937 0.0743482 0.0377308 -0.139008 0.0682751 0.0331050 -0.0866526 -0.0383130 0.0117850 -0.0240952 0.0400393 0.0809940 -0.0429156 -0.159851 0.0126200 0.0106298 -0.0212809 0.00295026 -0.00943432 0.0602099 -0.0644152 0.00000 0.0531561 0.00504124 0.107394 -0.0116981 0.177808 -0.0275010 0.0632146 -0.0964148 0.0448545 0.0930555 -0.0329402 0.233815 0.111024 0.684505 0.00020: 2829: -1024.14;0.452709 -0.0579625 -0.0344969 0.0120118 0.0734510 0.0896992 0.0499010 0.0954442 0.229133 0.00559951 -0.0696637 -0.0242122 0.111817 -0.0789206 0.0837580 0.222271 -0.0611380 -0.112126 0.0329055 -0.0277847 -0.00797160 0.120486 -0.0643358 -0.0845771 0.0736635 0.288591 0.257914 -0.00181955 0.110898 0.147722 0.00000 0.000172809 0.00919736 0.0110891 0.0152384 0.00828677 0.748922 -0.123172 0.330259 0.206775 0.0476522 0.0280362 -0.101461 0.0463152 0.0216698 0.0248654 0.170800 0.0176330 -0.0380416 0.0317560 0.142628 -0.0190826 -0.0671519 -0.00752021 0.00000 -0.0188540 0.0493985 -0.0278924 0.112063 -0.127799 0.00000 0.0437870 -0.0209737 0.0658322 -0.0777777 0.0965694 -0.0408791 0.127360 -0.174456 0.00000 -0.000274027 -0.0130361 2.24477e-05 9.26975e-05 0.0325827 2.0e-05: 5773: -1024.50;0.453311 -0.0585468 -0.0356171 0.0124542 0.0734133 0.0881771 0.0507491 0.0961264 0.230039 0.00643291 -0.0684440 -0.0227084 0.110856 -0.0735117 0.0881374 0.222234 -0.0600243 -0.108611 0.0331596 -0.0250605 -0.00237720 0.120825 -0.0585146 -0.0847106 0.0774464 0.290086 0.261511 -3.75224e-05 0.115623 0.142874 0.00000 0.000183608 0.000936919 0.000697002 0.000660236 0.00131320 0.749940 -0.124134 0.330879 0.212274 0.0477193 0.0297672 -0.103379 0.0384115 0.0255082 0.0174492 0.192128 0.0200405 -0.0419245 0.0218963 0.182316 -0.0385164 0.00556693 -0.0984504 0.00132058 -0.0348845 0.0586401 -0.0444801 0.190766 -0.212638 0.00000 0.000668956 -0.000403147 0.00142120 -0.00175281 0.00000 0.000166863 -0.00106768 0.000914863 0.000467674 -0.00274150 0.00346480 0.00114500 -0.000318531 0.00111171 2.0e-06: 9248: -1024.51;0.453348 -0.0584731 -0.0355746 0.0125052 0.0734903 0.0881831 0.0507601 0.0960166 0.230127 0.00645362 -0.0684865 -0.0230384 0.110978 -0.0737085 0.0876321 0.222118 -0.0599844 -0.108724 0.0331173 -0.0250216 -0.00259363 0.120773 -0.0589481 -0.0846303 0.0772936 0.289112 0.261172 -0.000167296 0.115547 0.144054 9.37833e-07 -2.73741e-05 -4.00219e-05 1.07567e-05 3.85261e-05 3.10490e-05 0.750009 -0.124225 0.330861 0.212679 0.0477609 0.0298412 -0.103074 0.0371887 0.0264153 0.0144220 0.197224 0.0205217 -0.0404589 0.0134622 0.191228 -0.0472840 0.0469882 -0.142964 0.0109281 -0.0403486 0.0179550 -0.0346377 0.183949 -0.183214 0.00214351 -0.00362890 0.00276037 -0.0118387 0.0131118 0.00000 5.56133e-05 -0.000257805 0.000368822 0.00000 0.000199027 -0.000336293 0.000352925 -0.000454433 1.25530e-05 2.0e-07: 15764: -1024.51;0.453351 -0.0584700 -0.0355723 0.0124965 0.0734787 0.0881846 0.0507616 0.0960397 0.230123 0.00645416 -0.0684847 -0.0230381 0.110983 -0.0737035 0.0876777 0.222123 -0.0599873 -0.108719 0.0331109 -0.0250217 -0.00255763 0.120773 -0.0589676 -0.0846268 0.0772896 0.289120 0.261188 -0.000185005 0.115550 0.144119 2.65656e-06 -6.82831e-06 -2.76466e-05 0.00000 1.83385e-05 7.87228e-07 0.750014 -0.124224 0.330867 0.212670 0.0477592 0.0298445 -0.103084 0.0371768 0.0264019 0.0144639 0.197132 0.0205263 -0.0404874 0.0135545 0.191202 -0.0472145 0.0466104 -0.142610 0.0108195 -0.0403564 0.0185109 -0.0348318 0.184403 -0.183997 0.000797901 -0.00138066 0.00103545 -0.00442881 0.00490172 0.00000 7.51833e-06 -3.87226e-05 6.04092e-05 0.00000 3.63510e-06 -4.49410e-06 1.42910e-06 5.74795e-06 5.03001e-07 At return 30433: -1024.5110: 0.453352 -0.0584699 -0.0355712 0.0124974 0.0734792 0.0881850 0.0507622 0.0960430 0.230123 0.00645469 -0.0684872 -0.0230377 0.110983 -0.0737051 0.0876725 0.222122 -0.0599864 -0.108715 0.0331096 -0.0250228 -0.00255738 0.120773 -0.0589583 -0.0846263 0.0772945 0.289133 0.261192 -0.000180489 0.115545 0.144090 0.00000 -1.01443e-06 -2.72331e-06 4.36692e-09 2.82744e-07 1.08531e-07 0.750012 -0.124223 0.330865 0.212669 0.0477582 0.0298448 -0.103086 0.0371794 0.0264002 0.0144699 0.197126 0.0205267 -0.0404964 0.0135892 0.191188 -0.0471837 0.0464530 -0.142440 0.0107821 -0.0403469 0.0186941 -0.0348872 0.184494 -0.184176 0.000119925 -0.000204327 0.000154724 -0.000663551 0.000735230 6.17347e-06 -3.20551e-06 1.16271e-05 -1.43359e-05 0.00000 1.40493e-06 -2.16589e-06 1.27346e-06 -2.57135e-06 2.74006e-08 *The model summary is as follows:* Linear mixed model fit by REML ['lmerMod'] Formula: Stimulus.RRT ~ 1 + A * B * C + (1 + A | Subject) + (1 + A * B * C | Item) + (1 + A * B * C | Category) Data: edata Control: lmerControl(optCtrl = list(maxfun = 5e+05)) REML criterion at convergence: -1024.5 Scaled residuals: Min 1Q Median 3Q Max -4.1135 -0.6290 -0.0239 0.6658 3.4528 Random effects: Groups Name Variance Std.Dev. Corr Item (Intercept) .0079856 0.08936 A1 0.0021904 0.04680 -0.25 B1 0.0019678 0.04436 -0.16 0.07 C1 0.0008949 0.02991 0.08 -0.46 -0.42 A1:B1 0.0034753 0.05895 0.25 -0.14 -0.40 0.04 A1:C1 0.0011016 0.03319 0.52 0.51 0.13 -0.73 0.10 B1:C1 0.0010864 0.03296 0.30 -0.50 -0.21 0.65 0.68 -0.39 A1:B1:C1 0.0047121 0.06864 0.28 0.18 -0.04 0.57 0.25 -0.11 0.64 Subject (Intercept) 0.0218561 0.14784 A1 0.0048530 0.06966 -0.35 Category (Intercept) 0.0017573 0.04192 A1 0.0001050 0.01025 0.92 B1 0.0001028 0.01014 0.58 0.22 C1 0.0004833 0.02198 -0.92 -0.80 -0.71 A1:B1 0.0014875 0.03857 0.19 0.56 -0.64 -0.09 A1:C1 0.0001609 0.01268 0.41 0.09 0.70 -0.27 -0.69 B1:C1 0.0014145 0.03761 0.08 0.17 0.05 -0.39 0.34 -0.67 A1:B1:C1 0.0036161 0.06013 0.65 0.41 0.62 -0.44 -0.39 0.93 -0.65 Residual 0.0388540 0.19711 Number of obs: 4064, groups: Item, 68; Subject, 64; Category, 6 Fixed effects: Estimate Std. Error t value (Intercept) -0.897455 0.027600 -32.52 A1 0.002163 0.012815 0.17 B1 -0.085623 0.038088 -2.25 C1 0.004287 0.014429 0.30 A1:B1 -0.005849 0.027516 -0.21 A1:C1 0.112263 0.075240 1.49 B1:C1 0.014424 0.026637 0.54 A1:B1:C1 -0.275033 0.152135 -1.81 Correlation of Fixed Effects: (Intr) A1 B1 C1 A1:B1 A1:C B1:C1 A1 -0.017 B1 0.030 0.012 C1 -0.349 -0.216 -0.064 A1:B1 0.093 0.091 -0.271 -0.028 A1:C1 0.028 0.014 0.006 -0.230 -0.027 B1:C1 0.044 0.000 -0.001 -0.114 0.138 -0.029 A1:B1:C1 0.071 0.025 0.010 -0.036 -0.033 0.010 -0.279 *However, if I reorder the dataset, the model failed to converge:* *edata.reordered<-edata[order(edata$Subject,edata$Item),]* *Model2<-lmer(Stimulus.RT~ 1+1+A*B*C+(1+A|Subject)+(1+A*B*C|Item)+(1+A*B*C|Category), data=edata.reordered,verbose=2,control=lmerControl(optCtrl=list(maxfun=500000)))* *The model failed to converge, with the following warning messages given. The verbose results are as follows:* npt = 77 , n = 75 rhobeg = 0.2 , rhoend = 2e-07 0.020: 86: -783.903;0.492578 -0.0462110 -0.0363706 -0.0207690 -0.00431843 0.00131644 -0.00481740 -0.00308283 0.502089 -0.0211198 -0.0336018 -0.0237567 -0.00155808 -0.0307396 -0.000390386 0.354830 -0.0308058 -0.0351383 -0.0137964 -0.0238208 -0.0133714 0.305507 -0.0174871 -0.0328382 -0.00155512 0.00996183 0.646703 -0.0258383 -0.00140789 -0.00146090 0.593574 -0.0176632 -0.0351208 0.611448 0.000434827 0.819591 0.735976 -0.105154 0.553306 0.948775 -0.00950248 -0.00976528 -0.0113362 -0.00949456 -0.00980388 -0.0100146 -0.00779947 0.946928 -0.00984788 -0.0101504 -0.00957164 -0.00986887 -0.00987130 -0.00960832 0.946926 -0.0101303 -0.0103410 -0.00976277 -0.00978197 -0.00937871 0.947412 -0.00988796 -0.0100200 -0.0103097 -0.0103002 0.949607 -0.0106541 -0.00943222 -0.0111189 0.947582 -0.0104333 -0.00883065 0.949409 -0.0119380 0.954371 0.0020: 1139: -1011.00;0.450616 -0.0596810 -0.0402272 0.0225926 0.0713467 0.103440 0.0602312 0.0721565 0.235672 0.00831575 -0.0664559 -0.0410181 0.131944 -0.0611257 0.0909471 0.234925 -0.0677961 -0.0928579 0.0491887 -0.0160378 -0.0609188 0.111951 -0.0797617 -0.0797398 0.105130 0.265564 0.270820 0.0170410 0.124769 0.199425 0.00278578 0.0268160 -0.00875111 0.0510349 0.110946 0.171222 0.758551 -0.121379 0.340244 0.289164 0.0340301 0.0299344 -0.127966 0.0945493 -0.0521814 -0.0136706 0.0808901 0.00103150 0.00478129 -0.0168369 -0.0419573 0.0154279 -0.0318584 -0.0284232 0.0113707 -0.00386646 -0.00455879 0.0266343 -0.0767034 0.0166990 0.0104083 -0.0298060 0.0336969 -0.0939880 -0.0530341 0.234749 -0.0630262 0.0236229 -0.0798100 0.0822270 -0.0883803 0.0469641 0.374075 0.0619410 0.530030 0.00020: 2754: -1024.04;0.453973 -0.0580443 -0.0341541 0.0128765 0.0743429 0.0876562 0.0513254 0.0961985 0.231256 0.00668522 -0.0689680 -0.0198556 0.111337 -0.0720035 0.0878909 0.222762 -0.0592635 -0.109626 0.0323470 -0.0244422 -0.00152948 0.121270 -0.0558188 -0.0840473 0.0772543 0.292009 0.262116 0.000440403 0.116106 0.141180 0.00136894 -0.00339338 -0.00197460 0.00348747 0.0105328 0.0115974 0.749943 -0.123029 0.330931 0.211777 0.0482195 0.0264188 -0.104392 0.0473197 0.0235507 0.0216781 0.184163 0.00000 0.0428526 -0.0310683 -0.175357 0.0368789 0.00217481 0.0939259 0.00000 0.0261004 -0.0313647 0.0268754 -0.122563 0.125346 0.000144427 -0.00203618 -0.000134387 -0.00154909 -0.000171925 0.0680554 -0.0371162 0.127778 -0.153718 0.0109013 -0.0471354 0.0631039 0.0408311 -0.0496230 0.0145855 2.0e-05: 5427: -1024.35;0.453500 -0.0588233 -0.0356824 0.0125644 0.0734100 0.0880990 0.0508688 0.0959347 0.230738 0.00618009 -0.0685843 -0.0222441 0.111017 -0.0734275 0.0869347 0.222257 -0.0600188 -0.109157 0.0332115 -0.0247808 -0.00219908 0.120720 -0.0585300 -0.0847411 0.0775846 0.290674 0.261498 -0.000106363 0.115210 0.143180 9.04281e-05 0.000517393 -0.000649503 0.000192640 0.000530472 0.00197453 0.749767 -0.124101 0.330961 0.212375 0.0487225 0.0278073 -0.102167 0.0463200 0.0243823 0.0162048 0.190955 0.00000 0.0439928 -0.0318335 -0.176979 0.0374637 0.00573705 0.0905893 0.00202642 0.0324678 -0.0734993 0.0480481 -0.190881 0.220526 0.000424046 -0.000319556 0.000485731 -0.00232483 0.00320987 0.00183945 -0.000969938 0.00300023 -0.00391019 0.00000 0.00377342 -0.00500665 0.000579337 -0.00215229 0.00124792 2.0e-06: 6708: -1024.35;0.453448 -0.0588097 -0.0355748 0.0125304 0.0734757 0.0881016 0.0508944 0.0958161 0.230754 0.00612699 -0.0684914 -0.0222696 0.110948 -0.0734877 0.0875664 0.222282 -0.0600764 -0.109357 0.0332592 -0.0250735 -0.00216874 0.120772 -0.0592389 -0.0846883 0.0772328 0.289264 0.261555 -0.000223966 0.115482 0.143552 1.82883e-06 8.95088e-05 0.000234107 4.38268e-06 -0.000100557 0.000268633 0.750021 -0.124161 0.330928 0.212373 0.0486257 0.0278246 -0.102272 0.0464224 0.0241039 0.0167711 0.190122 0.00000 0.0439680 -0.0319783 -0.176695 0.0374161 0.00595413 0.0898001 0.00213386 0.0324226 -0.0734879 0.0479364 -0.190868 0.220519 0.000515865 -0.000947565 0.000694358 -0.00286088 0.00324899 0.00000 9.25677e-05 -0.000493308 0.000515791 0.00000 3.22998e-05 1.34428e-06 0.000265348 -0.000330170 0.00000 2.0e-07: 8013: -1024.35;0.453452 -0.0588158 -0.0355775 0.0125249 0.0734807 0.0881052 0.0508818 0.0958264 0.230755 0.00613428 -0.0684918 -0.0222859 0.110940 -0.0735018 0.0875183 0.222287 -0.0600648 -0.109338 0.0332510 -0.0250861 -0.00212940 0.120774 -0.0592879 -0.0846939 0.0772195 0.289318 0.261524 -0.000225946 0.115515 0.143581 2.96479e-06 4.74658e-07 -4.67167e-06 6.32876e-07 -1.68484e-05 1.97228e-05 0.750021 -0.124164 0.330935 0.212387 0.0486304 0.0278256 -0.102275 0.0464074 0.0241141 0.0168024 0.190120 0.00000 0.0439746 -0.0319812 -0.176684 0.0373949 0.00599897 0.0897709 0.00214402 0.0324037 -0.0735321 0.0479302 -0.190866 0.220458 0.000500648 -0.000957101 0.000681481 -0.00281640 0.00318786 3.86116e-05 -1.87433e-05 6.35711e-05 -8.10232e-05 2.14600e-06 -9.10076e-06 1.52841e-05 1.48079e-05 -2.33962e-05 4.43378e-06 At return 9308: -1024.3493: 0.453452 -0.0588161 -0.0355779 0.0125253 0.0734813 0.0881053 0.0508814 0.0958291 0.230755 0.00613352 -0.0684914 -0.0222835 0.110941 -0.0734998 0.0875198 0.222286 -0.0600641 -0.109337 0.0332509 -0.0250859 -0.00212406 0.120774 -0.0592844 -0.0846942 0.0772196 0.289324 0.261524 -0.000224812 0.115519 0.143582 0.00000 6.69837e-07 2.90331e-06 5.81219e-07 -6.03136e-07 3.60563e-06 0.750021 -0.124164 0.330934 0.212389 0.0486307 0.0278253 -0.102276 0.0464083 0.0241164 0.0168007 0.190123 0.00000 0.0439749 -0.0319808 -0.176684 0.0373947 0.00599638 0.0897735 0.00214274 0.0324038 -0.0735262 0.0479285 -0.190864 0.220444 0.000501759 -0.000958368 0.000680990 -0.00282345 0.00319184 6.93762e-06 -2.82172e-06 7.79924e-06 -1.11704e-05 1.20920e-08 2.23002e-06 -2.13124e-06 2.39686e-06 -2.38538e-06 8.82173e-08 *Warning messages:* *1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :* * Model failed to converge with max|grad| = 0.761054 (tol = 0.002, component 54)* *2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :* * Model failed to converge: degenerate Hessian with 1 negative eigenvalues* *The model summary is as follows:* Linear mixed model fit by REML ['lmerMod'] Formula: Stimulus.RRT ~ 1 + RanOrCat * Direction * Running + (1 + RanOrCat | Subject) + (1 + RanOrCat * Direction * Running | Item) + (1 + RanOrCat * Direction * Running | Category) Data: Edata.Correct.NoBadItems.reordered Control: lmerControl(optCtrl = list(maxfun = 5e+05)) REML criterion at convergence: -1024.3 Scaled residuals: Min 1Q Median 3Q Max -4.1137 -0.6293 -0.0207 0.6657 3.4540 Random effects: Groups Name Variance Std.Dev. Corr Item (Intercept) 7.989e-03 0.089382 RanOrCat1 2.203e-03 0.046939 -0.25 Direction1 1.970e-03 0.044390 -0.16 0.07 Running1 8.953e-04 0.029921 0.08 -0.46 -0.42 RanOrCat1:Direction 3.488e-03 0.059055 0.25 -0.13 -0.40 0.04 RanOrCat1:Running1 1.101e-03 0.033188 0.52 0.51 0.13 -0.73 0.11 Direction1:Running1 1.085e-03 0.032941 0.30 -0.50 > -0.21 0.65 0.67 -0.39 RanOrCat1:Direction1:Running1 4.708e-03 0.068615 0.28 0.18
-0.04 0.57 0.25 - -0.11 0.64
Subject (Intercept) 2.186e-02 0.147840 RanOrCat1 4.854e-03 0.069672 -0.35 Category (Intercept) 1.753e-03 0.041865 RanOrCat1 9.189e-05 0.009586 1.00 Direction1 1.054e-04 0.010266 0.53 0.53 Running1 4.870e-04 0.022067 -0.91 -0.91 -0.72 RanOrCat1:Direction1 1.507e-03 0.038816 0.24 0.24 -0.65 -0.07 RanOrCat1:Running1 1.662e-04 0.012892 0.37 0.37 0.71 -0.29 -0.70 Direction1:Running1 1.428e-03 0.037790 0.09 0.09 0.03 -0.38 0.36 -0.68 RanOrCat1:Direction1:Running1 3.606e-03 0.060051 0.62 0.62
0.61 -0.44 -0.39 0.93 - -0.66
Residual 3.885e-02 0.197114 Number of obs: 4064, groups: Item, 68; Subject, 64; Category, 6 Fixed effects: Estimate Std. Error t value (Intercept) -0.897433 0.027587 -32.53 RanOrCat1 0.002115 0.012737 0.17 Direction1 -0.085626 0.038095 -2.25 Running1 0.004295 0.014451 0.30 RanOrCat1:Direction1 -0.005863 0.027579 -0.21 RanOrCat1:Running1 0.112270 0.075247 1.49 Direction1:Running1 0.014433 0.026681 0.54 RanOrCat1:Direction1:Running1 -0.275019 0.152130 -1.81 Correlation of Fixed Effects: (Intr) RnOrC1 Drctn1 Rnnng1 RnOC1:D1 ROC1:R Dr1:R1 RanOrCat1 -0.014 Direction1 0.028 0.023 Running1 -0.345 -0.229 -0.065 RnOrCt1:Dr1 0.109 0.028 -0.272 -0.020 RnOrCt1:Rn1 0.027 0.020 0.007 -0.231 -0.028 Drctn1:Rnn1 0.049 -0.016 -0.002 -0.111 0.145 -0.030 RnOC1:D1:R1 0.068 0.034 0.010 -0.036 -0.032 0.010 -0.279 *If we compare the two model summaries, we can see that the model summaries are really similar, with very little differences in values. Nevertheless, because Model2 failed to converge, I don't know if the model summary is still reliable and ok to report.* *Thank you for your time and I truly welcome and appreciate your comments.* Best, Zhaohong
-----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) iQEcBAEBAgAGBQJUrgPHAAoJEOCV5YRblxUH6qIIAJYKp9Djg4a8mf22WqIJ7tE6 /GzQTfdknwQgXwvMHbUP47YEE/mU0ms92zWwj6gvwKG+NsHmGmOrn+qGgqCS9LzC fI1ou5nYLrcSb2If+aXPWkQ2VYKbxyCh+ycuwbTqV5tz2LAEujbZYCKck+ZSXXFS UbazesAW618+A7VRIUWIuUtDSBA6qSchXjSZTsrosmCCS8JVtI5N2eOR/uCP3atn SPOPSPUy33hZaMLzZHpzSM6EV/nC9CAJGTaBtiSCiWbJbMeQiHiHJ9Nyk65sPoSS XOFfAl5FffLao6QWOWMRkD1q8eF8Y8hdagpniClxEvDc793zewzWALpBtXAgI0A= =VEtd -----END PGP SIGNATURE-----